formalisms.yaml

formalisms:
- id: pca
  name: Principal Component Analysis
  year: 1901
  origin: Pearson; Hotelling (1933)
  signature:
    operation: project
    domain: vector
    codomain: vector
    objective_family: none
  meso_type: linear_projection
  macro_type: eigenvalue_problem
  canonical_reference: Pearson, K. (1901). On lines and planes of closest fit.
  status: seed
- id: cca
  name: Canonical Correlation Analysis
  year: 1936
  origin: Hotelling
  signature:
    operation: project
    domain: vector
    codomain: vector
    objective_family: correlation
  meso_type: joint_embedding
  macro_type: eigenvalue_problem
  canonical_reference: Hotelling, H. (1936). Relations between two sets of variates.
  status: seed
- id: kernel_cca
  name: Kernel Canonical Correlation Analysis
  year: 2002
  origin: Bach & Jordan
  signature:
    operation: project
    domain: vector
    codomain: vector
    objective_family: correlation
  meso_type: kernel_method
  macro_type: eigenvalue_problem
  canonical_reference: Bach, F.R. & Jordan, M.I. (2002). Kernel independent component analysis. JMLR.
  status: seed
- id: ica
  name: Independent Component Analysis
  year: 1994
  origin: Comon
  signature:
    operation: decompose
    domain: vector
    codomain: vector
    objective_family: information
  meso_type: linear_projection
  macro_type: optimization
  canonical_reference: Comon, P. (1994). Independent component analysis, a new concept?
  status: seed
- id: fisher_lda
  name: Fisher Linear Discriminant Analysis
  year: 1936
  origin: Fisher
  signature:
    operation: project
    domain: vector
    codomain: vector
    objective_family: none
  meso_type: linear_projection
  macro_type: eigenvalue_problem
  canonical_reference: Fisher, R.A. (1936). The use of multiple measurements in taxonomic problems.
  status: seed
- id: mds
  name: Multi-Dimensional Scaling
  year: 1952
  origin: Torgerson
  signature:
    operation: project
    domain: matrix
    codomain: vector
    objective_family: none
  meso_type: spectral_method
  macro_type: eigenvalue_problem
  canonical_reference: 'Torgerson, W.S. (1952). Multidimensional scaling: I. Theory and method.'
  status: seed
- id: isomap
  name: Isometric Feature Mapping
  year: 2000
  origin: Tenenbaum, de Silva & Langford
  signature:
    operation: project
    domain: manifold
    codomain: vector
    objective_family: none
  meso_type: spectral_method
  macro_type: eigenvalue_problem
  canonical_reference: Tenenbaum, J.B., de Silva, V., & Langford, J.C. (2000). A global geometric framework for nonlinear
    dimensionality reduction. Science.
  status: seed
- id: lle
  name: Locally Linear Embedding
  year: 2000
  origin: Roweis & Saul
  signature:
    operation: project
    domain: manifold
    codomain: vector
    objective_family: none
  meso_type: spectral_method
  macro_type: eigenvalue_problem
  canonical_reference: Roweis, S.T. & Saul, L.K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science.
  status: seed
- id: laplacian_eigenmaps
  name: Laplacian Eigenmaps
  year: 2003
  origin: Belkin & Niyogi
  signature:
    operation: project
    domain: manifold
    codomain: vector
    objective_family: energy
  meso_type: spectral_method
  macro_type: eigenvalue_problem
  canonical_reference: Belkin, M. & Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and data representation.
    Neural Computation.
  researchor_artifact_id: laplacian_matrix
  status: seed
- id: svd
  name: Singular Value Decomposition
  year: 1873
  origin: Beltrami; Jordan; Golub & Reinsch
  signature:
    operation: decompose
    domain: matrix
    codomain: matrix
    objective_family: none
  meso_type: linear_projection
  macro_type: eigenvalue_problem
  canonical_reference: Golub, G.H. & Reinsch, C. (1970). Singular value decomposition and least squares solutions.
  researchor_artifact_id: singular_value_decomposition
  status: seed
- id: nmf
  name: Non-negative Matrix Factorization
  year: 1999
  origin: Lee & Seung
  signature:
    operation: decompose
    domain: matrix
    codomain: matrix
    objective_family: divergence
  meso_type: linear_projection
  macro_type: optimization
  canonical_reference: Lee, D.D. & Seung, H.S. (1999). Learning the parts of objects by non-negative matrix factorization.
    Nature.
  status: seed
  canonical_arxiv_id: 0408058
- id: kernel_pca
  name: Kernel Principal Component Analysis
  year: 1998
  origin: Schölkopf, Smola & Müller
  signature:
    operation: project
    domain: vector
    codomain: vector
    objective_family: none
  meso_type: kernel_method
  macro_type: eigenvalue_problem
  canonical_reference: Schölkopf, B., Smola, A., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue
    problem. Neural Computation.
  status: seed
- id: kernel_ridge_regression
  name: Kernel Ridge Regression
  year: 1998
  origin: Saunders, Gammerman & Vovk; extended by many
  signature:
    operation: transform
    domain: vector
    codomain: scalar
    objective_family: none
  meso_type: kernel_method
  macro_type: optimization
  canonical_reference: Saunders, C., Gammerman, A., & Vovk, V. (1998). Ridge regression learning algorithm in dual variables.
    ICML.
  status: seed
- id: gaussian_process
  name: Gaussian Process Regression
  year: 1996
  origin: Williams & Rasmussen; roots in kriging (Matheron 1963)
  signature:
    operation: transform
    domain: vector
    codomain: distribution
    objective_family: likelihood
  meso_type: kernel_method
  macro_type: statistical_inference
  canonical_reference: Rasmussen, C.E. & Williams, C.K.I. (2006). Gaussian Processes for Machine Learning. MIT Press.
  status: seed
- id: svm
  name: Support Vector Machine
  year: 1995
  origin: Cortes & Vapnik
  signature:
    operation: transform
    domain: vector
    codomain: assignment
    objective_family: none
  meso_type: kernel_method
  macro_type: optimization
  canonical_reference: Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning.
  status: seed
- id: mmd
  name: Maximum Mean Discrepancy
  year: 2006
  origin: Gretton, Borgwardt, Rasch, Schölkopf & Smola
  signature:
    operation: match
    domain: distribution
    codomain: scalar
    objective_family: none
  meso_type: kernel_method
  macro_type: statistical_inference
  canonical_reference: Gretton, A. et al. (2012). A kernel two-sample test. JMLR.
  status: seed
- id: kl_divergence_min
  name: KL Divergence Minimization
  year: 1951
  origin: Kullback & Leibler
  signature:
    operation: minimize
    domain: distribution
    codomain: distribution
    objective_family: divergence
  meso_type: information_geometry
  macro_type: optimization
  canonical_reference: Kullback, S. & Leibler, R.A. (1951). On information and sufficiency.
  researchor_artifact_id: relative_entropy
  status: seed
- id: mutual_info_max
  name: Mutual Information Maximization
  year: 1948
  origin: Shannon; McGill (1954)
  signature:
    operation: maximize
    domain: distribution
    codomain: scalar
    objective_family: information
  meso_type: information_geometry
  macro_type: optimization
  canonical_reference: Shannon, C.E. (1948). A mathematical theory of communication.
  researchor_artifact_id: mutual_information
  status: seed
- id: cross_entropy_min
  name: Cross-Entropy Minimization
  year: 1948
  origin: Shannon; Good (1956)
  signature:
    operation: minimize
    domain: distribution
    codomain: distribution
    objective_family: divergence
  meso_type: information_geometry
  macro_type: optimization
  canonical_reference: Good, I.J. (1956). The population frequencies of species and the estimation of population parameters.
  researchor_artifact_id: entropy
  status: seed
- id: infonce
  name: InfoNCE / Contrastive Estimation
  year: 2018
  origin: van den Oord, Li & Vinyals
  signature:
    operation: maximize
    domain: vector
    codomain: scalar
    objective_family: information
  meso_type: joint_embedding
  macro_type: optimization
  canonical_reference: van den Oord, A., Li, Y., & Vinyals, O. (2018). Representation learning with contrastive predictive
    coding. arXiv:1807.03748.
  status: seed
  canonical_arxiv_id: '1807.03748'
- id: js_divergence
  name: Jensen-Shannon Divergence
  year: 1991
  origin: Lin; extended from KL
  signature:
    operation: match
    domain: distribution
    codomain: scalar
    objective_family: divergence
  meso_type: information_geometry
  macro_type: statistical_inference
  canonical_reference: Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Trans. Info. Theory.
  status: seed
- id: elbo
  name: Evidence Lower BOund Maximization
  year: 1999
  origin: Jordan, Ghahramani, Jaakkola & Saul; variational Bayes
  signature:
    operation: maximize
    domain: distribution
    codomain: distribution
    objective_family: divergence
  meso_type: variational
  macro_type: optimization
  canonical_reference: Jordan, M.I. et al. (1999). An introduction to variational methods for graphical models. Machine Learning.
  status: seed
  canonical_arxiv_id: '1312.6114'
- id: variational_inference
  name: Variational Inference
  year: 1990
  origin: Hinton & van Camp; Jordan et al.; Wainwright & Jordan
  signature:
    operation: minimize
    domain: distribution
    codomain: distribution
    objective_family: divergence
  meso_type: variational
  macro_type: statistical_inference
  canonical_reference: Wainwright, M.J. & Jordan, M.I. (2008). Graphical models, exponential families, and variational inference.
    Foundations & Trends in ML.
  status: seed
- id: mean_field
  name: Mean-Field Approximation
  year: 1937
  origin: Landau; Weiss; applied to stat mech and later VI
  signature:
    operation: minimize
    domain: distribution
    codomain: distribution
    objective_family: divergence
  meso_type: mean_field
  macro_type: statistical_inference
  canonical_reference: Parisi, G. (1988). Statistical Field Theory. Addison-Wesley.
  status: seed
- id: mle
  name: Maximum Likelihood Estimation
  year: 1922
  origin: Fisher
  signature:
    operation: maximize
    domain: distribution
    codomain: scalar
    objective_family: likelihood
  meso_type: probabilistic_inference
  macro_type: statistical_inference
  canonical_reference: Fisher, R.A. (1922). On the mathematical foundations of theoretical statistics.
  status: seed
- id: map_estimation
  name: Maximum A Posteriori Estimation
  year: 1763
  origin: Bayes; Laplace
  signature:
    operation: maximize
    domain: distribution
    codomain: scalar
    objective_family: likelihood
  meso_type: probabilistic_inference
  macro_type: statistical_inference
  canonical_reference: Berger, J.O. (1985). Statistical Decision Theory and Bayesian Analysis. Springer.
  status: seed
- id: em_algorithm
  name: Expectation-Maximization
  year: 1977
  origin: Dempster, Laird & Rubin
  signature:
    operation: maximize
    domain: distribution
    codomain: distribution
    objective_family: likelihood
  meso_type: variational
  macro_type: optimization
  canonical_reference: Dempster, A.P., Laird, N.M., & Rubin, D.B. (1977). Maximum likelihood from incomplete data via the
    EM algorithm. JRSS-B.
  status: seed
- id: mcmc
  name: Markov Chain Monte Carlo
  year: 1953
  origin: Metropolis, Rosenbluth, Rosenbluth, Teller & Teller; Hastings (1970)
  signature:
    operation: sample
    domain: distribution
    codomain: sequence
    objective_family: none
  meso_type: probabilistic_inference
  macro_type: stochastic_process
  canonical_reference: Metropolis, N. et al. (1953). Equation of state calculations by fast computing machines.
  researchor_artifact_id: markov_chain
  status: seed
- id: empirical_bayes
  name: Empirical Bayes / Type-II Maximum Likelihood
  year: 1955
  origin: Robbins; Efron & Morris
  signature:
    operation: maximize
    domain: distribution
    codomain: distribution
    objective_family: likelihood
  meso_type: probabilistic_inference
  macro_type: statistical_inference
  canonical_reference: Robbins, H. (1955). An empirical Bayes approach to statistics. Proc. Third Berkeley Symp.
  status: seed
- id: normalizing_flow
  name: Normalizing Flow
  year: 2015
  origin: Rezende & Mohamed; Dinh, Krueger & Bengio (NICE 2014)
  signature:
    operation: transform
    domain: distribution
    codomain: distribution
    objective_family: likelihood
  meso_type: variational
  macro_type: statistical_inference
  canonical_reference: Rezende, D.J. & Mohamed, S. (2015). Variational inference with normalizing flows. ICML.
  status: seed
  canonical_arxiv_id: '1505.05770'
- id: boltzmann_distribution
  name: Boltzmann / Gibbs Distribution
  year: 1868
  origin: Boltzmann; Gibbs (1902)
  signature:
    operation: sample
    domain: scalar_field
    codomain: distribution
    objective_family: energy
  meso_type: energy_model
  macro_type: hamiltonian_system
  canonical_reference: Gibbs, J.W. (1902). Elementary Principles in Statistical Mechanics.
  researchor_mental_model_id: ergodicity
  status: seed
- id: free_energy_min
  name: Free Energy Minimization
  year: 1873
  origin: Helmholtz; Gibbs
  signature:
    operation: minimize
    domain: distribution
    codomain: distribution
    objective_family: energy
  meso_type: energy_model
  macro_type: hamiltonian_system
  canonical_reference: Helmholtz, H. (1882). Die Thermodynamik chemischer Vorgänge.
  researchor_mental_model_id: phase_transitions
  status: seed
- id: hopfield_network
  name: Hopfield Network / Spin Glass
  year: 1982
  origin: Hopfield; Sherrington & Kirkpatrick (1975 spin glass)
  signature:
    operation: minimize
    domain: vector
    codomain: assignment
    objective_family: energy
  meso_type: spin_system
  macro_type: hamiltonian_system
  canonical_reference: Hopfield, J.J. (1982). Neural networks and physical systems with emergent collective computational
    abilities. PNAS.
  researchor_mental_model_id: attractors_and_basins
  status: seed
- id: renormalization_group
  name: Renormalization Group
  year: 1971
  origin: Wilson; Kadanoff (block-spin 1966)
  signature:
    operation: transform
    domain: scalar_field
    codomain: scalar_field
    objective_family: none
  meso_type: energy_model
  macro_type: hamiltonian_system
  canonical_reference: Wilson, K.G. (1971). Renormalization group and critical phenomena. Phys. Rev. B.
  researchor_mental_model_id: scale_invariance
  status: seed
- id: langevin_dynamics
  name: Langevin Dynamics
  year: 1908
  origin: Langevin; adapted to sampling by Parisi (1981)
  signature:
    operation: sample
    domain: scalar_field
    codomain: distribution
    objective_family: energy
  meso_type: dynamical_system
  macro_type: stochastic_process
  canonical_reference: Langevin, P. (1908). Sur la théorie du mouvement brownien.
  researchor_artifact_id: brownian_motion
  status: seed
- id: hamiltonian_monte_carlo
  name: Hamiltonian Monte Carlo
  year: 1987
  origin: Duane, Kennedy, Pendleton & Roweth; Neal (2011 MCMC handbook)
  signature:
    operation: sample
    domain: distribution
    codomain: sequence
    objective_family: energy
  meso_type: dynamical_system
  macro_type: hamiltonian_system
  canonical_reference: Neal, R.M. (2011). MCMC using Hamiltonian dynamics. Handbook of Markov Chain Monte Carlo.
  status: seed
- id: diffusion_sde
  name: Diffusion Process / Reverse-Time SDE
  year: 2015
  origin: Sohl-Dickstein et al.; Ho, Jain & Abbeel (DDPM 2020); Song et al. (SDE 2021)
  signature:
    operation: sample
    domain: distribution
    codomain: distribution
    objective_family: none
  meso_type: diffusion_process
  macro_type: stochastic_process
  canonical_reference: Song, Y. et al. (2021). Score-based generative modeling through stochastic differential equations.
    ICLR.
  researchor_artifact_id: brownian_motion
  researchor_mental_model_id: irreversibility
  status: seed
  canonical_arxiv_id: '2011.13456'
- id: wasserstein_distance
  name: Wasserstein Distance / Earth Mover's Distance
  year: 1781
  origin: Monge; Kantorovich (1942)
  signature:
    operation: match
    domain: distribution
    codomain: scalar
    objective_family: none
  meso_type: optimal_transport
  macro_type: optimization
  canonical_reference: Kantorovich, L.V. (1942). On the translocation of masses.
  status: seed
- id: sinkhorn_algorithm
  name: Sinkhorn-Knopp Algorithm / Entropic OT
  year: 1967
  origin: Sinkhorn & Knopp; Cuturi (2013) for ML
  signature:
    operation: match
    domain: distribution
    codomain: matrix
    objective_family: divergence
  meso_type: optimal_transport
  macro_type: optimization
  canonical_reference: 'Cuturi, M. (2013). Sinkhorn distances: lightspeed computation of optimal transport. NeurIPS.'
  status: seed
  canonical_arxiv_id: '1306.0895'
- id: kantorovich_dual
  name: Kantorovich Duality
  year: 1942
  origin: Kantorovich
  signature:
    operation: maximize
    domain: distribution
    codomain: scalar
    objective_family: none
  meso_type: optimal_transport
  macro_type: optimization
  canonical_reference: 'Villani, C. (2008). Optimal Transport: Old and New. Springer.'
  status: seed
- id: gradient_descent
  name: Gradient Descent
  year: 1847
  origin: Cauchy
  signature:
    operation: minimize
    domain: scalar_field
    codomain: vector
    objective_family: none
  meso_type: none
  macro_type: optimization
  canonical_reference: Cauchy, A. (1847). Méthode générale pour la résolution des systèmes d'équations simultanées.
  researchor_artifact_id: gradient_descent
  status: seed
- id: sgd
  name: Stochastic Gradient Descent
  year: 1951
  origin: Robbins & Monro
  signature:
    operation: minimize
    domain: scalar_field
    codomain: vector
    objective_family: none
  meso_type: none
  macro_type: optimization
  canonical_reference: Robbins, H. & Monro, S. (1951). A stochastic approximation method.
  researchor_artifact_id: stochastic_gradient_descent
  status: seed
- id: lagrange_multiplier
  name: Constrained Optimization (Lagrange Multiplier)
  year: 1788
  origin: Lagrange
  signature:
    operation: minimize
    domain: scalar_field
    codomain: vector
    objective_family: none
  meso_type: none
  macro_type: optimization
  canonical_reference: Lagrange, J.-L. (1788). Mécanique Analytique.
  status: seed
- id: proximal_gradient
  name: Proximal Gradient Method
  year: 2005
  origin: Combettes & Wajs; Beck & Teboulle (FISTA 2009)
  signature:
    operation: minimize
    domain: scalar_field
    codomain: vector
    objective_family: none
  meso_type: none
  macro_type: optimization
  canonical_reference: Beck, A. & Teboulle, M. (2009). A fast iterative shrinkage-thresholding algorithm. SIAM J. Imaging
    Sci.
  status: seed
- id: adam
  name: Adam Optimizer
  year: 2014
  origin: Kingma & Ba
  signature:
    operation: minimize
    domain: scalar_field
    codomain: vector
    objective_family: none
  meso_type: none
  macro_type: optimization
  canonical_reference: 'Kingma, D.P. & Ba, J. (2015). Adam: a method for stochastic optimization. ICLR.'
  status: seed
  canonical_arxiv_id: '1412.6980'
- id: softmax_attention
  name: Softmax Attention / Weighted Aggregation
  year: 2014
  origin: Bahdanau, Cho & Bengio; Vaswani et al. (Transformer 2017)
  signature:
    operation: aggregate
    domain: sequence
    codomain: vector
    objective_family: none
  meso_type: none
  macro_type: none
  canonical_reference: Vaswani, A. et al. (2017). Attention is all you need. NeurIPS.
  status: seed
  canonical_arxiv_id: '1706.03762'
- id: residual_connection
  name: Residual Connection / Skip Connection
  year: 2016
  origin: He, Zhang, Ren & Sun; earlier in Hochreiter & Schmidhuber (LSTM 1997)
  signature:
    operation: transform
    domain: vector
    codomain: vector
    objective_family: none
  meso_type: none
  macro_type: none
  canonical_reference: He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. CVPR.
  status: seed
  canonical_arxiv_id: '1512.03385'
- id: batch_normalization
  name: Batch Normalization
  year: 2015
  origin: Ioffe & Szegedy
  signature:
    operation: transform
    domain: vector
    codomain: vector
    objective_family: none
  meso_type: none
  macro_type: none
  canonical_reference: 'Ioffe, S. & Szegedy, C. (2015). Batch normalization: accelerating deep network training. ICML.'
  status: seed
  canonical_arxiv_id: '1502.03167'
- id: vae
  name: Variational Autoencoder
  year: 2013
  origin: Kingma & Welling; Rezende, Mohamed & Wierstra
  signature:
    operation: transform
    domain: vector
    codomain: distribution
    objective_family: divergence
  meso_type: variational
  macro_type: statistical_inference
  canonical_reference: Kingma, D.P. & Welling, M. (2014). Auto-encoding variational Bayes. ICLR.
  status: seed
  canonical_arxiv_id: '1312.6114'
- id: gan
  name: Generative Adversarial Network
  year: 2014
  origin: Goodfellow et al.
  signature:
    operation: minimize
    domain: distribution
    codomain: distribution
    objective_family: adversarial
  meso_type: game_theoretic
  macro_type: optimization
  canonical_reference: Goodfellow, I.J. et al. (2014). Generative adversarial nets. NeurIPS.
  researchor_mental_model_id: nash_equilibrium
  status: seed
  canonical_arxiv_id: '1406.2661'
- id: contrastive_learning
  name: Contrastive Learning
  year: 2005
  origin: Chopra, Hadsell & LeCun (Siamese, 2005); Hadsell et al. (dimensionality reduction 2006)
  signature:
    operation: minimize
    domain: vector
    codomain: scalar
    objective_family: energy
  meso_type: joint_embedding
  macro_type: optimization
  canonical_reference: Hadsell, R., Chopra, S., & LeCun, Y. (2006). Dimensionality reduction by learning an invariant mapping.
    CVPR.
  researchor_mental_model_id: signal_vs_noise
  status: seed
- id: knowledge_distillation
  name: Knowledge Distillation
  year: 2015
  origin: Hinton, Vinyals & Dean
  signature:
    operation: minimize
    domain: distribution
    codomain: distribution
    objective_family: divergence
  meso_type: none
  macro_type: optimization
  canonical_reference: Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. NeurIPS workshop.
  status: seed
  canonical_arxiv_id: '1503.02531'
- id: layer_normalization
  name: Layer Normalization
  year: 2016
  origin: Ba, Kiros & Hinton
  signature:
    operation: transform
    domain: vector
    codomain: vector
    objective_family: none
  meso_type: none
  macro_type: none
  canonical_reference: Ba, J.L., Kiros, J.R., & Hinton, G.E. (2016). Layer normalization. arXiv:1607.06450.
  status: seed
  canonical_arxiv_id: '1607.06450'
- id: ridge_regression
  name: Ridge Regression (L2 Regularization)
  year: 1970
  origin: Hoerl & Kennard; Tikhonov (1943)
  signature:
    operation: minimize
    domain: vector
    codomain: scalar
    objective_family: none
  meso_type: none
  macro_type: optimization
  canonical_reference: 'Hoerl, A.E. & Kennard, R.W. (1970). Ridge regression: biased estimation for nonorthogonal problems.'
  status: seed
- id: lasso
  name: LASSO (L1 Regularization)
  year: 1996
  origin: Tibshirani
  signature:
    operation: minimize
    domain: vector
    codomain: scalar
    objective_family: none
  meso_type: none
  macro_type: optimization
  canonical_reference: Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. JRSS-B.
  status: seed
- id: elastic_net
  name: Elastic Net
  year: 2005
  origin: Zou & Hastie
  signature:
    operation: minimize
    domain: vector
    codomain: scalar
    objective_family: none
  meso_type: none
  macro_type: optimization
  canonical_reference: Zou, H. & Hastie, T. (2005). Regularization and variable selection via the elastic net. JRSS-B.
  status: seed
- id: spectral_clustering
  name: Spectral Clustering
  year: 2000
  origin: Shi & Malik (normalized cuts); Ng, Jordan & Weiss (2002)
  signature:
    operation: decompose
    domain: graph
    codomain: assignment
    objective_family: none
  meso_type: spectral_method
  macro_type: eigenvalue_problem
  canonical_reference: 'Ng, A.Y., Jordan, M.I., & Weiss, Y. (2002). On spectral clustering: analysis and an algorithm. NeurIPS.'
  researchor_artifact_id: laplacian_matrix
  status: seed
- id: pagerank
  name: PageRank
  year: 1998
  origin: Page, Brin, Motwani & Winograd
  signature:
    operation: propagate
    domain: graph
    codomain: vector
    objective_family: none
  meso_type: spectral_method
  macro_type: eigenvalue_problem
  canonical_reference: 'Page, L. et al. (1999). The PageRank citation ranking: bringing order to the web.'
  researchor_artifact_id: adjacency_matrix
  researchor_mental_model_id: network_centrality
  status: seed
- id: fourier_transform
  name: Fourier Transform / Spectral Decomposition
  year: 1822
  origin: Fourier; Cooley & Tukey (FFT 1965)
  signature:
    operation: decompose
    domain: sequence
    codomain: sequence
    objective_family: none
  meso_type: spectral_method
  macro_type: none
  canonical_reference: Fourier, J.B.J. (1822). Théorie analytique de la chaleur.
  researchor_mental_model_id: signal_vs_noise
  status: seed